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Niche battery/energy specialization reduces applicants despite metro location and mid-level experience.
Role mixes transferable ML skills with specialized battery and energy domain knowledge, limiting cross-industry fit.
Explicit 3–7 years, domain-specific energy/battery experience, and production ML skills enforce strict filters.
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Develop and maintain advanced analytics, machine learning, and optimization models for assessing and controlling battery energy storage system performance, including forecasting and predictive maintenance.
Create visualizations and reports to communicate model results, performance metrics, and insights to stakeholders.
Collaborate with cross-functional teams including product managers, data scientists, and software engineers to integrate analytics solutions into company platforms.
Bachelor’s degree in Data Science, Computer Science, Electrical/Mechanical/Chemical Engineering, Mathematics, or related field.
3 to 7+ years of professional experience in data science, software engineering, controls, operations research, or related roles in energy sector or industrial automation.
Proficiency in Python and scientific libraries (NumPy, Pandas, Scikit-learn, TensorFlow, Pyomo, Plotly) with experience delivering scalable, production-grade analytics solutions.
Solid understanding of energy sector with familiarity in battery storage and renewable energy technologies.
Experienced in building and deploying data-driven models for battery storage or renewable energy applications with a blend of domain and technical expertise.
Proven ability to work independently and collaboratively with global, cross-functional teams in dynamic project environments.
Strong analytical, problem-solving, and communication skills to translate complex data insights into actionable business and operational improvements.